Triple

T32165303
Position Surface form Disambiguated ID Type / Status
Subject Ricki Lake E821554 entity
Predicate birthName P65 FINISHED
Object Ricki Pamela Lake
Ricki Pamela Lake is an American actress and television host best known for her breakout role in the film "Hairspray" and for hosting her long-running daytime talk show "Ricki Lake."
E1997372 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Ricki Pamela Lake | Statement: [Ricki Lake, birthName, Ricki Pamela Lake]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ricki Pamela Lake
Triple: [Ricki Lake, birthName, Ricki Pamela Lake]
Generated description
Ricki Pamela Lake is an American actress and television host best known for her breakout role in the film "Hairspray" and for hosting her long-running daytime talk show "Ricki Lake."

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f3490699a48190bbef96b198e8fade completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6ba1f7c008190932bf0da8e41c73f completed May 3, 2026, 2:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f3b7fec388190aed876067b22da08 completed June 14, 2026, 11:38 p.m.
NEDg Description generation batch_6a2f3f51482c8190b6da7a7d17ba4e66 completed June 14, 2026, 11:54 p.m.
NED2 Entity disambiguation (via description) batch_6a2f3fd37958819094adc116cde5a94a completed June 14, 2026, 11:57 p.m.
Created at: May 1, 2026, 12:33 a.m.